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Assistant Ai Strategy Jobs in Romeoville, IL (NOW HIRING)

CNA

Senior AI Software Engineer

Chicago, IL · On-site

$126K - $166K/yr

At CNA, we strive to create a culture in which people know they matter and are part of something ... rollout strategies that protect production while enabling continuous delivery. * Embed ...

Cna

Senior AI Software Engineer

Chicago, IL

$126K - $166K/yr

At CNA, we strive to create a culture in which people know they matter and are part of something ... rollout strategies that protect production while enabling continuous delivery. * Embed ...

CNA

System Architect Director - AI Innovation

Chicago, IL · On-site

$250K/yr

At CNA, we strive to create a culture in which people know they matter and are part of something ... Scaled Agile (SAFe) leadership-decomposing strategy into Enabler Epics, shaping PI outcomes ...

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All JobsAi Strategy JobsAssistant Ai Strategy JobsAssistant Ai Strategy Jobs in Romeoville, IL

Assistant Ai Strategy information

See Romeoville, IL salary details

$56.6K

$127.1K

$221.8K

How much do assistant ai strategy jobs pay per year?

As of Aug 23, 2026, the average yearly pay for assistant ai strategy in Romeoville, IL is $127,103.00, according to ZipRecruiter salary data. Most workers in this role earn between $91,800.00 and $160,600.00 per year, depending on experience, location, and employer.

What is an assistant AI strategy?

An Assistant AI Strategy refers to the planning and implementation of artificial intelligence technologies, such as digital assistants, to support and enhance business operations. This role involves analyzing current AI trends, identifying opportunities for AI integration, and recommending best practices for deploying AI assistants. The goal is to improve efficiency, automate routine tasks, and provide valuable insights for decision-making. Professionals in this field work closely with technical teams and business stakeholders to align AI initiatives with organizational objectives.

What are some typical challenges an assistant AI strategy professional might face when supporting the development and implementation of AI initiatives?

As an Assistant AI Strategy professional, you'll often encounter challenges such as aligning AI initiatives with overall business objectives, managing stakeholder expectations, and navigating evolving technologies. You may also need to balance the need for innovation with concerns about data privacy, ethical use, and regulatory compliance. Collaborating effectively across technical, business, and operations teams is crucial to ensure the successful integration of AI solutions within existing workflows.

What are the key skills and qualifications needed to thrive as an assistant AI strategist, and why are they important?

To thrive as an Assistant AI Strategist, you need a solid understanding of artificial intelligence concepts, data analysis, and strategic planning, often supported by a degree in computer science, data science, or business. Familiarity with tools like Python, machine learning platforms (such as TensorFlow or Azure ML), and data visualization software is commonly required. Strong communication, critical thinking, and collaborative skills help you effectively bridge technical teams and business stakeholders. These skills ensure successful AI project implementation and alignment with organizational goals.

What is the difference between Assistant Ai Strategy vs Data Analyst?

AspectAssistant Ai StrategyData Analyst
Required CredentialsBachelor's in Computer Science, AI, or related fields; familiarity with AI toolsBachelor's in Statistics, Data Science, or related fields; proficiency in data analysis tools
Work EnvironmentTech companies, AI startups, R&D teamsBusiness, finance, healthcare, and other industries
Employer & Industry UsagePrimarily in AI-focused roles within tech and innovation sectorsAcross various industries for data-driven decision making

The Assistant Ai Strategy role focuses on developing and implementing AI strategies, working closely with AI teams, and understanding AI technologies. In contrast, a Data Analyst primarily interprets data, creates reports, and supports business decisions through data insights. While both roles require analytical skills, Assistant Ai Strategy emphasizes AI knowledge and strategic planning, whereas Data Analysts focus on data manipulation and analysis.

How do I become an assistant AI strategist?

To become an assistant AI strategist, develop a strong foundation in artificial intelligence, machine learning, and data analysis through relevant degrees or certifications. Gaining experience with AI tools, programming languages like Python, and understanding business applications of AI can enhance your qualifications for this role.

What is the easiest assistant AI strategy job to get into?

Entry-level assistant AI strategy roles typically require a basic understanding of AI concepts, data analysis, and familiarity with tools like Python or machine learning frameworks. Candidates often start with internships or related positions in data analysis or AI support, and building a foundational knowledge through online courses or certifications can improve chances of entry.

What job categories do people searching Assistant Ai Strategy jobs in Romeoville, IL look for?

The top searched job categories for Assistant Ai Strategy jobs in Romeoville, IL are:

What cities near Romeoville, IL are hiring for Assistant Ai Strategy jobs?

Cities near Romeoville, IL with the most Assistant Ai Strategy job openings:

Assistant Ai Strategy jobs near you

System Architect Director - AI Platform Engineering

CNA

Chicago, IL • On-site

Full-time

Re-posted 14 days ago

Job description

You have a clear vision of where your career can go. And we have the leadership to help you get there. At CNA, we strive to create a culture in which people know they matter and are part of something important, ensuring the abilities of all employees are used to their fullest potential.
The System Architect (SA) Director for AI Platforms Engineering serves as the technical owner for the enterprise AI platform which is the shared foundation powering all AI and GenAI products across the organization. This leader owns the platform's architecture, engineering standards, and delivery roadmap, translating strategic AI capabilities into reliable, scalable, and governed platform capabilities that accelerate every product team building on top of them.
Working in close partnership with Enterprise Architects, Product Management, and Release Train Engineers (RTEs), the SA Director ensures that platform investments are tightly aligned to business outcomes, compliance requirements, and engineering excellence. This role combines the strategic depth of a principal architect with the hands-on leadership of a delivery-focused engineering director.
JOB DESCRIPTION:
Essential Duties & Responsibilities
Performs a combination of duties in accordance with departmental guidelines:
  • Own and continuously evolve the enterprise AI Platform reference architecture, encompassing all critical layers including model serving, orchestration engines, data and knowledge grounding pipelines, observability infrastructure, and ensuring the platform scales reliably to enterprise-grade workloads and usage patterns.

  • Define and enforce platform-wide standards, reusable design patterns, and golden-path templates that enable product and feature teams to build, deploy, and operate AI solutions safely, consistently, and with significantly reduced time-to-production.

  • Drive end-to-end delivery of new platform capabilities - from initial technical discovery and architecture design through prototyping, hardening, and full production rollout while maintaining meaningful hands-on involvement at critical technical milestones to ensure quality and coherence.

  • Architect and operationalize the core platform service catalog, including LLM gateway and routing layers, prompt lifecycle management, agentic orchestration frameworks, Retrieval-Augmented Generation (RAG) pipelines, vector stores, model registries, and rigorous automated evaluation infrastructure.

  • Build and maintain robust CI/CD and AIOps pipelines specifically designed for AI systems, incorporating automated evaluation gates, model and data versioning controls, staged deployment promotion, and continuous cost and performance optimization guardrails.

  • Architect enterprise-grade multi-agent and single-agent workflow patterns for high-value business use cases, establishing clear standards for orchestration design, state and memory management, tool and API integration, and safe autonomy controls including human-in-the-loop approvals, permission scoping, and comprehensive audit trails.

  • Design and implement knowledge grounding systems - spanning hybrid retrieval strategies, semantic reranking, ontology-driven entity modeling, and knowledge graph integration - to measurably improve AI output accuracy, traceability, and readiness for regulatory audit.

  • Embed responsible AI and compliance-by-design principles into every layer of the platform, covering data privacy protections, enterprise secrets management, granular access controls, output leakage prevention, and model risk governance practices aligned to enterprise and regulatory standards.

  • Actively shape PI Planning by authoring well-defined Enabler Epics and articulating architectural outcomes that anchor near-term delivery and long-horizon platform capability roadmaps, while contributing expert WSJF input to balance platform investment against feature team needs, risk reduction, and time-to-impact.

  • Directly manage, mentor, and grow a high-performing team of platform engineers, solution architects, and technical specialists - hiring hands-on builders, coaching technical leadership skills, and sustaining a healthy innovation pipeline that continuously advances the organization's AI platform maturity.

May perform additional duties as assigned.
Skills, Knowledge & Abilities
  • Deep AI Platform and AIOps engineering expertise, including hands-on experience designing, deploying, and operating shared AI platform capabilities such as model serving layers, LLM gateway and proxy services, prompt registries, vector databases, and automated evaluation harnesses at enterprise scale.
  • Proven agentic system design capability, with hands-on experience architecting multi-agent and single-agent workflow systems using orchestration frameworks such as Lang Graph, Google ADK - including tool and function calling patterns, state and memory persistence strategies, and robust safe autonomy controls.
  • Applied GenAI depth spanning LLM solution architecture patterns, model selection and routing strategies, advanced prompt engineering techniques, fine-tuning and RLHF tradeoffs, and production-grade RAG and hybrid retrieval system design and optimization.
  • Strong cloud-native and distributed systems architecture skills, with deep GCP expertise across Vertex AI, Cloud Run, GKE, Pub/Sub, and BigQuery, and a solid command of API and service-based design, event-driven architecture, and high-availability and fault-tolerant system patterns.
  • Knowledge grounding and semantic layer proficiency, including experience building canonical ontology and entity models, designing vector search and hybrid retrieval pipelines, integrating knowledge graphs, implementing reranking strategies, and establishing citation and traceability mechanisms that support compliance.
  • Solid AIOps and platform reliability engineering experience, including CI/CD pipeline design for AI systems, automated evaluation and quality gates, model and dataset versioning, production monitoring and observability, reliability engineering practices, and systematic cost-performance optimization.
  • Practical responsible AI and security expertise, with demonstrated experience implementing enterprise AI governance frameworks, model risk management programs, PII and data privacy controls, audit and event logging, and compliance-by-design patterns suited to regulated industries.
  • Strong SDLC and hands-on engineering fundamentals, including Python proficiency, architectural and code review practices, comprehensive testing strategies for AI systems, technical debt management, refactoring discipline, and operational readiness standards.
  • Scaled Agile (SAFe) leadership experience, including decomposing long-horizon strategy into actionable Enabler Epics, shaping PI planning outcomes.
  • Exceptional leadership and communication skills, with a demonstrated ability to influence senior stakeholders and cross-functional teams, negotiate complex technology tradeoffs, mentor and develop engineers at all levels, and translate deep technical concepts into compelling narratives for non-technical business audiences.

Education & Experience
  • Bachelor's degree in Computer Science, Software Engineering, Information Technology, or equivalent required; Master's degree in AI, Machine Learning, Data Science, or related discipline strongly preferred.
  • 10+ years in software engineering and technical delivery, with demonstrated ownership of large-scale, distributed enterprise systems across the full SDLC from inception through production operations.
  • 5+ years in system or solution architecture, with a track record of producing reference architectures, design patterns, technical standards, and enterprise-scale platform guardrails.
  • 5+ years of direct people leadership, including hiring, performance management, career development, and building high-performing engineering and architecture teams.
  • 5+ years hands-on designing, delivering, and operating AI/ML or GenAI platform capabilities in production, with measurable outcomes in quality, reliability, and developer adoption.
  • Strong Python proficiency and deep practical GCP experience - Vertex AI, GCP Agent Builder, and Gemini - with the ability to engage credibly in hands-on technical work alongside the engineering team.
  • Prior experience in regulated industries (insurance, financial services, or healthcare) strongly preferred, given stringent governance, auditability, and model risk management requirements.
  • Consulting or enterprise delivery background is a plus, bringing structured problem-solving and stakeholder management

#LI-KJ1 #LI-HYBRID
In certain jurisdictions, CNA is legally required to include a reasonable estimate of the compensation for this role. In District of Columbia, California, Colorado, Connecticut, Illinois, Maryland, Massachusetts, New York and Washington, the national base pay range for this job level is $97,000 to $189,000 annually. Salary determinations are based on various factors, including but not limited to, relevant work experience, skills, certifications and location. CNA offers a comprehensive and competitive benefits package to help our employees - and their family members - achieve their physical, financial, emotional and social wellbeing goals. For a detailed look at CNA's benefits, please visit cnabenefits.com.
CNA utilizes AI-enabled technology during the recruiting process. For more information, please visit our careers page.
CNA is committed to providing reasonable accommodations to qualified individuals with disabilities in the recruitment process. To request an accommodation, please contact leaveadministration@cna.com

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